In computer image analysis such as intelligent transportation systems, a common task is to consistently classify and label places in a captured image scene. For example, place recognition is the task of consistently labeling a particular place (e.g., “kitchen on 2nd floor with a coffee machine”) every time the place is visited, while place categorization is to consistently label places according to their category (e.g., “kitchen”, “living room”). Place recognition and categorization are important for a robot or an intelligent agent to recognize places in a manner similar to that done by humans.
Most existing place recognition systems assume a finite set of place labels, which are learned offline from supervised training data. Some existing place recognition systems use place classifiers, which categorize places during runtime based on some measurements of input data. For example, one type of place recognition method models local features and distinctive parts of input images. Alternatively, a place recognition method extracts global representations of input images and learns place categories from the global representations of the images.
Existing place recognition systems face a variety of challenges including the requirement of large training data and limited place recognition (e.g., only recognizing places known from training data). For example, existing place recognition methods in robotics range from matching scale-invariant feature transform (SIFT) features across images to other derived measures of distinctiveness for places such as Fourier signatures, subspace representations and color histograms. These methods have the disadvantage of not being able to generalize and also are invariant to perspective mainly through the use of omnidirectional images.